Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles

Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2015) Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles. Statistical Applications in Genetics and Molecular Biology, 14(2), pp. 143-167. (doi:10.1515/sagmb-2014-0041) (PMID:25719342)

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Publisher's URL: http://dx.doi.org/10.1515/sagmb-2014-0041

Abstract

There has been much interest in reconstructing bi-directional regulatory networks linking the circadian clock to metabolism in plants. A variety of reverse engineering methods from machine learning and computational statistics have been proposed and evaluated. The emphasis of the present paper is on combining models in a model ensemble to boost the network reconstruction accuracy, and to explore various model combination strategies to maximize the improvement. Our results demonstrate that a rich ensemble of predictors outperforms the best individual model, even if the ensemble includes poor predictors with inferior individual reconstruction accuracy. For our application to metabolomic and transcriptomic time series from various mutagenesis plants grown in different light-dark cycles we also show how to determine the optimal time lag between interactions, and we identify significant interactions with a randomization test. Our study predicts new statistically significant interactions between circadian clock genes and metabolites in Arabidopsis thaliana, and thus provides independent statistical evidence that the regulation of metabolism by the circadian clock is not uni-directional, but that there is a statistically significant feedback mechanism aiming from metabolism back to the circadian clock.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Husmeier, Professor Dirk and Aderhold, Mr Andrej
Authors: Grzegorczyk, M., Aderhold, A., and Husmeier, D.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Statistical Applications in Genetics and Molecular Biology
Publisher:De Gruyter
ISSN:2194-6302
ISSN (Online):1544-6115
Copyright Holders:Copyright © 2015 De Gruyter
First Published:First published in Statistical Applications in Genetics and Molecular Biology 14(2):143-167
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
592371TIMET: Linking the clock to metabolismDirk HusmeierEuropean Commission (EC)FP7 245143 TIMEM&S - STATISTICS